James Knight | Searcharxiv

arXiv2026

Emergent Control of Ant Learning Walks in a Mobile Robot

Before foraging, desert ants perform learning walks (LWs) to acquire visual memories around their nest. With experience, these walks expand, become straighter, and contain fewer visual scans. We previously proposed a model in which these behaviours emerge from the angular mismatch between path integration (PI) and a mushroom-body-inspired visual compass (VC). Here, we test whether this mechanism can generate learning walks on a physical robot, despite the sensory and motor noise inherent to real-world conditions. We implement the model on a four-wheeled robot equipped with a panoramic camera, an IMU and wheel encoders providing compass and stride information for PI, and test it outdoors using a replicated desert-ant experimental setup. The PI--VC mismatch regulates walking speed, turning, pirouettes, and returns to the nest, while PI provides a teaching signal for visual learning. The robot autonomously decides when to acquire visual memories, while its behaviour gradually shifts from exploration towards foraging as visual learning progresses. The robot reproduces desert-ant LW features, including expanding and straightening trajectories, decreasing pirouette frequency, and improved visual homing. These results demonstrate that a simple error-driven mechanism can generate key features of learning walks under real-world conditions, supporting its plausibility as an explanation for active visual learning in desert ants. More broadly, this work illustrates how physical robots can be used to test biological models.

cs.RO↗

arXiv2024

Learning Delays Through Gradients and Structure: Emergence of Spatiotemporal Patterns in Spiking Neural Networks

We present a Spiking Neural Network (SNN) model that incorporates learnable synaptic delays through two approaches: per-synapse delay learning via Dilated Convolutions with Learnable Spacings (DCLS) and a dynamic pruning strategy that also serves as a form of delay learning. In the latter approach, the network dynamically selects and prunes connections, optimizing the delays in sparse connectivity settings. We evaluate both approaches on the Raw Heidelberg Digits keyword spotting benchmark using Backpropagation Through Time with surrogate gradients. Our analysis of the spatio-temporal structure of synaptic interactions reveals that, after training, excitation and inhibition group together in space and time. Notably, the dynamic pruning approach, which employs DEEP R for connection removal and RigL for reconnection, not only preserves these spatio-temporal patterns but outperforms per-synapse delay learning in sparse networks. Our results demonstrate the potential of combining delay learning with dynamic pruning to develop efficient SNN models for temporal data processing. Moreover, the preservation of spatio-temporal dynamics throughout pruning and rewiring highlights the robustness of these features, providing a solid foundation for future neuromorphic computing applications.

cs.NE↗